Motivated by the need for faster and more accurate methods for estimating VI with NN, we propose a computationally efficient VI estimate inspired by the lazy training framework of [1]} that estimates the difference between the full model parameters and the model parameters when the \(j\) -th variable is removed. Like dropout, our procedure only requires us to train the NN once on the full data, and then we solve a linear system to update the full model parameters for each variable \(j \in [p]\) .
